Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations600
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory142.5 KiB
Average record size in memory243.2 B

Variable types

Categorical2
Numeric15

Alerts

Account_Balance is highly overall correlated with Spending_RateHigh correlation
Age is highly overall correlated with Customer_IDHigh correlation
Customer_ID is highly overall correlated with Age and 11 other fieldsHigh correlation
Debt_Payments is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Debt_Ratio is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Essential_Spending is highly overall correlated with Customer_ID and 2 other fieldsHigh correlation
Gender is highly overall correlated with Customer_IDHigh correlation
Income is highly overall correlated with Customer_ID and 2 other fieldsHigh correlation
Investment_Rate is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Investments_Amount is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Non_Essential_Spending is highly overall correlated with Spending_VolatilityHigh correlation
Savings_Amount is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Savings_Rate is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Spending_Rate is highly overall correlated with Account_BalanceHigh correlation
Spending_Volatility is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Total_Spending is highly overall correlated with Customer_ID and 2 other fieldsHigh correlation
Customer_ID is uniformly distributed Uniform
Gender is uniformly distributed Uniform
Savings_Amount has 344 (57.3%) zeros Zeros
Investments_Amount has 284 (47.3%) zeros Zeros
Debt_Payments has 180 (30.0%) zeros Zeros
Savings_Rate has 344 (57.3%) zeros Zeros
Investment_Rate has 284 (47.3%) zeros Zeros
Debt_Ratio has 180 (30.0%) zeros Zeros

Reproduction

Analysis started2025-07-27 01:11:02.635463
Analysis finished2025-07-27 01:11:27.529290
Duration24.89 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Customer_ID
Categorical

High correlation  Uniform 

Distinct50
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
CUST1001
 
12
CUST1040
 
12
CUST1022
 
12
CUST1028
 
12
CUST1017
 
12
Other values (45)
540 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUST1001
2nd rowCUST1001
3rd rowCUST1001
4th rowCUST1001
5th rowCUST1001

Common Values

ValueCountFrequency (%)
CUST1001 12
 
2.0%
CUST1040 12
 
2.0%
CUST1022 12
 
2.0%
CUST1028 12
 
2.0%
CUST1017 12
 
2.0%
CUST1018 12
 
2.0%
CUST1041 12
 
2.0%
CUST1014 12
 
2.0%
CUST1009 12
 
2.0%
CUST1011 12
 
2.0%
Other values (40) 480
80.0%

Length

2025-07-27T01:11:27.608642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cust1001 12
 
2.0%
cust1040 12
 
2.0%
cust1022 12
 
2.0%
cust1028 12
 
2.0%
cust1017 12
 
2.0%
cust1018 12
 
2.0%
cust1041 12
 
2.0%
cust1014 12
 
2.0%
cust1009 12
 
2.0%
cust1011 12
 
2.0%
Other values (40) 480
80.0%

Most occurring characters

ValueCountFrequency (%)
1 780
16.2%
0 780
16.2%
U 600
12.5%
C 600
12.5%
T 600
12.5%
S 600
12.5%
4 180
 
3.8%
2 180
 
3.8%
3 180
 
3.8%
8 60
 
1.2%
Other values (4) 240
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 780
16.2%
0 780
16.2%
U 600
12.5%
C 600
12.5%
T 600
12.5%
S 600
12.5%
4 180
 
3.8%
2 180
 
3.8%
3 180
 
3.8%
8 60
 
1.2%
Other values (4) 240
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 780
16.2%
0 780
16.2%
U 600
12.5%
C 600
12.5%
T 600
12.5%
S 600
12.5%
4 180
 
3.8%
2 180
 
3.8%
3 180
 
3.8%
8 60
 
1.2%
Other values (4) 240
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 780
16.2%
0 780
16.2%
U 600
12.5%
C 600
12.5%
T 600
12.5%
S 600
12.5%
4 180
 
3.8%
2 180
 
3.8%
3 180
 
3.8%
8 60
 
1.2%
Other values (4) 240
 
5.0%

Month
Real number (ℝ)

Distinct12
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-27T01:11:27.685225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4549328
Coefficient of variation (CV)0.53152813
Kurtosis-1.2169172
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum3900
Variance11.936561
MonotonicityNot monotonic
2025-07-27T01:11:27.764018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 50
8.3%
2 50
8.3%
3 50
8.3%
4 50
8.3%
5 50
8.3%
6 50
8.3%
7 50
8.3%
8 50
8.3%
9 50
8.3%
10 50
8.3%
Other values (2) 100
16.7%
ValueCountFrequency (%)
1 50
8.3%
2 50
8.3%
3 50
8.3%
4 50
8.3%
5 50
8.3%
6 50
8.3%
7 50
8.3%
8 50
8.3%
9 50
8.3%
10 50
8.3%
ValueCountFrequency (%)
12 50
8.3%
11 50
8.3%
10 50
8.3%
9 50
8.3%
8 50
8.3%
7 50
8.3%
6 50
8.3%
5 50
8.3%
4 50
8.3%
3 50
8.3%

Age
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.14
Minimum23
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:27.853555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile24
Q135
median41
Q352
95-th percentile59
Maximum59
Range36
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.591854
Coefficient of variation (CV)0.25134918
Kurtosis-0.98637138
Mean42.14
Median Absolute Deviation (MAD)7
Skewness-0.0055677092
Sum25284
Variance112.18738
MonotonicityIncreasing
2025-07-27T01:11:27.954173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
41 60
 
10.0%
38 36
 
6.0%
57 36
 
6.0%
59 36
 
6.0%
34 36
 
6.0%
35 24
 
4.0%
47 24
 
4.0%
54 24
 
4.0%
56 24
 
4.0%
42 24
 
4.0%
Other values (19) 276
46.0%
ValueCountFrequency (%)
23 24
4.0%
24 12
 
2.0%
25 12
 
2.0%
27 24
4.0%
28 12
 
2.0%
30 12
 
2.0%
32 12
 
2.0%
34 36
6.0%
35 24
4.0%
36 24
4.0%
ValueCountFrequency (%)
59 36
6.0%
58 12
 
2.0%
57 36
6.0%
56 24
4.0%
54 24
4.0%
53 12
 
2.0%
52 12
 
2.0%
51 12
 
2.0%
48 24
4.0%
47 24
4.0%

Gender
Categorical

High correlation  Uniform 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size36.5 KiB
Male
300 
Female
300 

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters3000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 300
50.0%
Female 300
50.0%

Length

2025-07-27T01:11:28.066368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-27T01:11:28.134351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 300
50.0%
female 300
50.0%

Most occurring characters

ValueCountFrequency (%)
e 900
30.0%
a 600
20.0%
l 600
20.0%
M 300
 
10.0%
F 300
 
10.0%
m 300
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 900
30.0%
a 600
20.0%
l 600
20.0%
M 300
 
10.0%
F 300
 
10.0%
m 300
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 900
30.0%
a 600
20.0%
l 600
20.0%
M 300
 
10.0%
F 300
 
10.0%
m 300
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 900
30.0%
a 600
20.0%
l 600
20.0%
M 300
 
10.0%
F 300
 
10.0%
m 300
 
10.0%

Income
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16310.74
Minimum6711
Maximum28829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:28.219723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6711
5-th percentile7998
Q111637
median14939.5
Q321071
95-th percentile27658
Maximum28829
Range22118
Interquartile range (IQR)9434

Descriptive statistics

Standard deviation5965.8444
Coefficient of variation (CV)0.36576172
Kurtosis-0.70414704
Mean16310.74
Median Absolute Deviation (MAD)3830
Skewness0.48532317
Sum9786444
Variance35591299
MonotonicityNot monotonic
2025-07-27T01:11:28.338604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11337 12
 
2.0%
10700 12
 
2.0%
13234 12
 
2.0%
15000 12
 
2.0%
17000 12
 
2.0%
19395 12
 
2.0%
16081 12
 
2.0%
17600 12
 
2.0%
13726 12
 
2.0%
14330 12
 
2.0%
Other values (40) 480
80.0%
ValueCountFrequency (%)
6711 12
2.0%
6895 12
2.0%
7998 12
2.0%
8068 12
2.0%
8305 12
2.0%
9645 12
2.0%
10343 12
2.0%
10700 12
2.0%
10727 12
2.0%
11012 12
2.0%
ValueCountFrequency (%)
28829 12
2.0%
28019 12
2.0%
27658 12
2.0%
27171 12
2.0%
26474 12
2.0%
25794 12
2.0%
23929 12
2.0%
23827 12
2.0%
23734 12
2.0%
21998 12
2.0%

Total_Spending
Real number (ℝ)

High correlation 

Distinct581
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14128.112
Minimum3765.35
Maximum28790.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:28.475448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3765.35
5-th percentile6021.213
Q19881.205
median13428.51
Q318555.615
95-th percentile25248.571
Maximum28790.15
Range25024.8
Interquartile range (IQR)8674.41

Descriptive statistics

Standard deviation5841.684
Coefficient of variation (CV)0.41347945
Kurtosis-0.40512078
Mean14128.112
Median Absolute Deviation (MAD)3834.79
Skewness0.53845798
Sum8476867.3
Variance34125272
MonotonicityNot monotonic
2025-07-27T01:11:28.602646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18637.41 3
 
0.5%
18634.4 2
 
0.3%
7900 2
 
0.3%
28790.15 2
 
0.3%
28157.15 2
 
0.3%
15409 2
 
0.3%
16000 2
 
0.3%
19299.25 2
 
0.3%
14600 2
 
0.3%
15456.4 2
 
0.3%
Other values (571) 579
96.5%
ValueCountFrequency (%)
3765.35 1
0.2%
3981.64 1
0.2%
4088.67 1
0.2%
4361.74 1
0.2%
4373.45 1
0.2%
4983.92 1
0.2%
4988.49 1
0.2%
5028.35 1
0.2%
5040.45 1
0.2%
5042.82 1
0.2%
ValueCountFrequency (%)
28790.15 2
0.3%
28780.15 1
0.2%
28697.89 1
0.2%
28697.15 1
0.2%
28490.15 1
0.2%
28447.15 1
0.2%
28440.15 1
0.2%
28233.15 1
0.2%
28157.15 2
0.3%
28124.15 1
0.2%

Essential_Spending
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6227.5725
Minimum1500
Maximum11000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:28.723540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile3000
Q14500
median5500
Q37500
95-th percentile10508
Maximum11000
Range9500
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation2308.639
Coefficient of variation (CV)0.37071251
Kurtosis-0.57660493
Mean6227.5725
Median Absolute Deviation (MAD)1000
Skewness0.48724465
Sum3736543.5
Variance5329814.3
MonotonicityNot monotonic
2025-07-27T01:11:28.849731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500 72
 
12.0%
5500 61
 
10.2%
6500 48
 
8.0%
10000 36
 
6.0%
7500 25
 
4.2%
4900 24
 
4.0%
3500 24
 
4.0%
6000 24
 
4.0%
4800 24
 
4.0%
7000 13
 
2.2%
Other values (50) 249
41.5%
ValueCountFrequency (%)
1500 12
2.0%
2900 12
2.0%
3000 12
2.0%
3500 24
4.0%
3570 12
2.0%
3593.27 1
 
0.2%
3646.93 1
 
0.2%
4000 12
2.0%
4024.74 1
 
0.2%
4084.31 1
 
0.2%
ValueCountFrequency (%)
11000 12
 
2.0%
10950 12
 
2.0%
10508 12
 
2.0%
10456 1
 
0.2%
10000 36
6.0%
9800 13
 
2.2%
9500 1
 
0.2%
9450 12
 
2.0%
9000 12
 
2.0%
8600 12
 
2.0%

Non_Essential_Spending
Real number (ℝ)

High correlation 

Distinct547
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2698.3847
Minimum371.34
Maximum11600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:28.966038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum371.34
5-th percentile748.7735
Q11478.645
median2195
Q33580.5025
95-th percentile6384.045
Maximum11600
Range11228.66
Interquartile range (IQR)2101.8575

Descriptive statistics

Standard deviation1794.9142
Coefficient of variation (CV)0.66518099
Kurtosis2.9104119
Mean2698.3847
Median Absolute Deviation (MAD)901.14
Skewness1.5412559
Sum1619030.8
Variance3221717
MonotonicityNot monotonic
2025-07-27T01:11:29.096165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 6
 
1.0%
1567 6
 
1.0%
1900 4
 
0.7%
1728 3
 
0.5%
2750 3
 
0.5%
2500 3
 
0.5%
5100 3
 
0.5%
2800 3
 
0.5%
4567 3
 
0.5%
1200 3
 
0.5%
Other values (537) 563
93.8%
ValueCountFrequency (%)
371.34 1
0.2%
469.52 1
0.2%
470.6 1
0.2%
478.9 1
0.2%
500 2
0.3%
527.69 1
0.2%
528.35 1
0.2%
540.45 1
0.2%
574.08 1
0.2%
583.92 1
0.2%
ValueCountFrequency (%)
11600 1
0.2%
11500 1
0.2%
9600 1
0.2%
9170 1
0.2%
9100 2
0.3%
8300 2
0.3%
8150 1
0.2%
8000 1
0.2%
7950 1
0.2%
7890.19 1
0.2%

Savings_Amount
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1167.9336
Minimum0
Maximum8151.3
Zeros344
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:29.212126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31646.1
95-th percentile6321.3
Maximum8151.3
Range8151.3
Interquartile range (IQR)1646.1

Descriptive statistics

Standard deviation1931.3259
Coefficient of variation (CV)1.6536265
Kurtosis3.345798
Mean1167.9336
Median Absolute Deviation (MAD)0
Skewness1.9771297
Sum700760.14
Variance3730019.9
MonotonicityNot monotonic
2025-07-27T01:11:29.330160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 344
57.3%
3210 12
 
2.0%
566.85 12
 
2.0%
6321.3 12
 
2.0%
1099.9 12
 
2.0%
5280 12
 
2.0%
1700 12
 
2.0%
1199.7 12
 
2.0%
1646.1 12
 
2.0%
3499.2 12
 
2.0%
Other values (29) 148
24.7%
ValueCountFrequency (%)
0 344
57.3%
344.75 6
 
1.0%
415.25 1
 
0.2%
473.82 1
 
0.2%
566.85 12
 
2.0%
625.55 9
 
1.5%
771.38 1
 
0.2%
771.8 6
 
1.0%
800 3
 
0.5%
830.5 1
 
0.2%
ValueCountFrequency (%)
8151.3 12
2.0%
6517.8 12
2.0%
6321.3 12
2.0%
5280 12
2.0%
4351.64 1
 
0.2%
3989.8 12
2.0%
3499.2 12
2.0%
3210 12
2.0%
3162.05 1
 
0.2%
2966.64 1
 
0.2%

Investments_Amount
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1746.0659
Minimum0
Maximum8648.7
Zeros284
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:29.451546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median588.95
Q32379.795
95-th percentile7148.1
Maximum8648.7
Range8648.7
Interquartile range (IQR)2379.795

Descriptive statistics

Standard deviation2354.7778
Coefficient of variation (CV)1.3486191
Kurtosis1.496943
Mean1746.0659
Median Absolute Deviation (MAD)588.95
Skewness1.5120343
Sum1047639.5
Variance5544978.6
MonotonicityNot monotonic
2025-07-27T01:11:29.560585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 284
47.3%
1700.55 12
 
2.0%
7120.2 12
 
2.0%
2909.25 12
 
2.0%
850 12
 
2.0%
7148.1 12
 
2.0%
1651.8 12
 
2.0%
2145.4 12
 
2.0%
8648.7 12
 
2.0%
588.95 12
 
2.0%
Other values (27) 208
34.7%
ValueCountFrequency (%)
0 284
47.3%
254.67 1
 
0.2%
344.75 6
 
1.0%
588.95 12
 
2.0%
731.36 1
 
0.2%
850 12
 
2.0%
930.34 1
 
0.2%
982.46 1
 
0.2%
1112.35 1
 
0.2%
1251.1 8
 
1.3%
ValueCountFrequency (%)
8648.7 12
2.0%
8405.7 12
2.0%
7148.1 12
2.0%
7120.2 12
2.0%
6599.4 12
2.0%
4785.8 12
2.0%
4255.5 12
2.0%
3734.4 12
2.0%
3532 8
1.3%
3087.2 9
1.5%

Debt_Payments
Real number (ℝ)

High correlation  Zeros 

Distinct45
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2288.1547
Minimum0
Maximum10987
Zeros180
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:29.677440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1300
Q32500
95-th percentile9000
Maximum10987
Range10987
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation2904.9564
Coefficient of variation (CV)1.2695629
Kurtosis1.4880868
Mean2288.1547
Median Absolute Deviation (MAD)1300
Skewness1.5876288
Sum1372892.8
Variance8438771.6
MonotonicityNot monotonic
2025-07-27T01:11:29.793652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 180
30.0%
1200 60
 
10.0%
2300 36
 
6.0%
1800 31
 
5.2%
900 24
 
4.0%
9000 24
 
4.0%
1300 24
 
4.0%
1500 24
 
4.0%
5000 24
 
4.0%
3000 17
 
2.8%
Other values (35) 156
26.0%
ValueCountFrequency (%)
0 180
30.0%
2.98 1
 
0.2%
8.18 1
 
0.2%
11.73 1
 
0.2%
21.55 1
 
0.2%
25.16 1
 
0.2%
57.32 1
 
0.2%
77.39 1
 
0.2%
108.48 1
 
0.2%
131.38 1
 
0.2%
ValueCountFrequency (%)
10987 12
2.0%
10000 12
2.0%
9000 24
4.0%
7650 12
2.0%
7300 12
2.0%
7000 12
2.0%
5000 24
4.0%
3500 12
2.0%
3200 12
2.0%
3000 17
2.8%

Account_Balance
Real number (ℝ)

High correlation 

Distinct578
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2182.6279
Minimum-1.7
Maximum16884.26
Zeros2
Zeros (%)0.3%
Negative1
Negative (%)0.2%
Memory size4.8 KiB
2025-07-27T01:11:29.907749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7
5-th percentile38.85
Q1543.095
median1546.7
Q32853.775
95-th percentile6079.7365
Maximum16884.26
Range16885.96
Interquartile range (IQR)2310.68

Descriptive statistics

Standard deviation2497.263
Coefficient of variation (CV)1.1441543
Kurtosis10.241779
Mean2182.6279
Median Absolute Deviation (MAD)1119.705
Skewness2.7185604
Sum1309576.7
Variance6236322.4
MonotonicityNot monotonic
2025-07-27T01:11:30.030654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
757.59 3
 
0.5%
27 2
 
0.3%
13 2
 
0.3%
95.75 2
 
0.3%
599.6 2
 
0.3%
0 2
 
0.3%
671.85 2
 
0.3%
38.85 2
 
0.3%
94.75 2
 
0.3%
44.75 2
 
0.3%
Other values (568) 579
96.5%
ValueCountFrequency (%)
-1.7 1
0.2%
0 2
0.3%
0.25 1
0.2%
1.7 1
0.2%
4.7 1
0.2%
7 2
0.3%
7.75 1
0.2%
8 1
0.2%
8.2 2
0.3%
10.57 1
0.2%
ValueCountFrequency (%)
16884.26 1
0.2%
16525.3 1
0.2%
16149.76 1
0.2%
16005.68 1
0.2%
14463.17 1
0.2%
14264.01 1
0.2%
13833.95 1
0.2%
13567.12 1
0.2%
12660.75 1
0.2%
11592.94 1
0.2%

Savings_Rate
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070470179
Minimum0
Maximum0.32882273
Zeros344
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:30.139644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.3
Maximum0.32882273
Range0.32882273
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.10413898
Coefficient of variation (CV)1.4777737
Kurtosis0.22224778
Mean0.070470179
Median Absolute Deviation (MAD)0
Skewness1.2924276
Sum42.282107
Variance0.010844926
MonotonicityNot monotonic
2025-07-27T01:11:30.233164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 344
57.3%
0.3 72
 
12.0%
0.05 47
 
7.8%
0.1 34
 
5.7%
0.15 29
 
4.8%
0.2 25
 
4.2%
0.05 15
 
2.5%
0.1 12
 
2.0%
0.2 8
 
1.3%
0.04124774426 3
 
0.5%
Other values (11) 11
 
1.8%
ValueCountFrequency (%)
0 344
57.3%
0.03580323409 1
 
0.2%
0.04124774426 3
 
0.5%
0.05 15
 
2.5%
0.05 47
 
7.8%
0.05828774369 1
 
0.2%
0.1 12
 
2.0%
0.1 34
 
5.7%
0.1153732809 1
 
0.2%
0.1420265982 1
 
0.2%
ValueCountFrequency (%)
0.3288227293 1
 
0.2%
0.3 72
12.0%
0.2389338069 1
 
0.2%
0.224168052 1
 
0.2%
0.2215210821 1
 
0.2%
0.2135907511 1
 
0.2%
0.2 25
 
4.2%
0.2 8
 
1.3%
0.1793886958 1
 
0.2%
0.1504480883 1
 
0.2%

Investment_Rate
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093997409
Minimum0
Maximum0.3
Zeros284
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:30.321824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.05
Q30.1813998
95-th percentile0.3
Maximum0.3
Range0.3
Interquartile range (IQR)0.1813998

Descriptive statistics

Standard deviation0.10638135
Coefficient of variation (CV)1.1317477
Kurtosis-0.87325753
Mean0.093997409
Median Absolute Deviation (MAD)0.05
Skewness0.69366827
Sum56.398446
Variance0.011316992
MonotonicityNot monotonic
2025-07-27T01:11:30.899036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 284
47.3%
0.15 84
 
14.0%
0.2 68
 
11.3%
0.3 48
 
8.0%
0.05 30
 
5.0%
0.1 24
 
4.0%
0.3 24
 
4.0%
0.1 20
 
3.3%
0.2 9
 
1.5%
0.1775736739 1
 
0.2%
Other values (8) 8
 
1.3%
ValueCountFrequency (%)
0 284
47.3%
0.01924361493 1
 
0.2%
0.05 30
 
5.0%
0.05526371467 1
 
0.2%
0.07029922926 1
 
0.2%
0.0742375699 1
 
0.2%
0.08405244068 1
 
0.2%
0.1 20
 
3.3%
0.1 24
 
4.0%
0.1019706816 1
 
0.2%
ValueCountFrequency (%)
0.3 24
 
4.0%
0.3 48
8.0%
0.2 68
11.3%
0.2 9
 
1.5%
0.1928781925 1
 
0.2%
0.1775736739 1
 
0.2%
0.15 84
14.0%
0.1229265528 1
 
0.2%
0.1019706816 1
 
0.2%
0.1 24
 
4.0%

Debt_Ratio
Real number (ℝ)

High correlation  Zeros 

Distinct59
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12894565
Minimum0
Maximum0.55966669
Zeros180
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:31.007633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.10276532
Q30.1610973
95-th percentile0.42595177
Maximum0.55966669
Range0.55966669
Interquartile range (IQR)0.1610973

Descriptive statistics

Standard deviation0.13632679
Coefficient of variation (CV)1.0572422
Kurtosis0.87914465
Mean0.12894565
Median Absolute Deviation (MAD)0.10276532
Skewness1.2100976
Sum77.36739
Variance0.018584993
MonotonicityNot monotonic
2025-07-27T01:11:31.134408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180
30.0%
0.08411214953 12
 
2.0%
0.105848108 12
 
2.0%
0.4294117647 12
 
2.0%
0.1675652047 12
 
2.0%
0.3489183531 12
 
2.0%
0.1202889825 12
 
2.0%
0.5596666874 12
 
2.0%
0.154679041 12
 
2.0%
0.1533333333 12
 
2.0%
Other values (49) 312
52.0%
ValueCountFrequency (%)
0 180
30.0%
0.0002251775729 1
 
0.2%
0.0006538246343 1
 
0.2%
0.0008863533323 1
 
0.2%
0.001722484214 1
 
0.2%
0.001901163669 1
 
0.2%
0.00458156822 1
 
0.2%
0.006185756534 1
 
0.2%
0.008670769723 1
 
0.2%
0.009927459574 1
 
0.2%
ValueCountFrequency (%)
0.5596666874 12
2.0%
0.4294117647 12
2.0%
0.4259517717 12
2.0%
0.3588087549 12
2.0%
0.3509979353 12
2.0%
0.3489183531 12
2.0%
0.3468729404 12
2.0%
0.3212106071 12
2.0%
0.2644103649 12
2.0%
0.2122421258 12
2.0%

Spending_Rate
Real number (ℝ)

High correlation 

Distinct581
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86077412
Minimum0.38081512
Maximum1.0002126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:31.264131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.38081512
5-th percentile0.57695503
Q10.80315328
median0.89877246
Q30.96441249
95-th percentile0.99806411
Maximum1.0002126
Range0.61939743
Interquartile range (IQR)0.16125921

Descriptive statistics

Standard deviation0.13463487
Coefficient of variation (CV)0.15641138
Kurtosis1.6429377
Mean0.86077412
Median Absolute Deviation (MAD)0.075275997
Skewness-1.3882394
Sum516.46447
Variance0.018126548
MonotonicityNot monotonic
2025-07-27T01:11:31.383904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9609389018 3
 
0.5%
0.9982508422 2
 
0.3%
0.9950631606 2
 
0.3%
0.9991103812 2
 
0.3%
0.9766953415 2
 
0.3%
1 2
 
0.3%
0.962655705 2
 
0.3%
0.9986523986 2
 
0.3%
0.9932005741 2
 
0.3%
0.9967886616 2
 
0.3%
Other values (571) 579
96.5%
ValueCountFrequency (%)
0.3808151229 1
0.2%
0.3890861684 1
0.2%
0.389534312 1
0.2%
0.4025128353 1
0.2%
0.4160908236 1
0.2%
0.4196775609 1
0.2%
0.4213001663 1
0.2%
0.4272069779 1
0.2%
0.4275838093 1
0.2%
0.441261485 1
0.2%
ValueCountFrequency (%)
1.000212553 1
0.2%
1 2
0.3%
0.9999637418 1
0.2%
0.9999193204 1
0.2%
0.999622572 2
0.3%
0.9996004125 1
0.2%
0.9995731929 1
0.2%
0.9995333333 1
0.2%
0.9995253221 1
0.2%
0.9995033524 1
0.2%

Spending_Volatility
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125.7127
Minimum265.55936
Maximum3792.0142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2025-07-27T01:11:31.512394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum265.55936
5-th percentile282.00732
Q1600.50211
median930.99789
Q31574.5077
95-th percentile2410.8254
Maximum3792.0142
Range3526.4549
Interquartile range (IQR)974.00558

Descriptive statistics

Standard deviation707.31326
Coefficient of variation (CV)0.62832482
Kurtosis2.270901
Mean1125.7127
Median Absolute Deviation (MAD)371.18799
Skewness1.3120438
Sum675427.65
Variance500292.05
MonotonicityNot monotonic
2025-07-27T01:11:31.637493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
598.6192741 12
 
2.0%
358.7609616 12
 
2.0%
600.5021111 12
 
2.0%
1463.164416 12
 
2.0%
660.7151543 12
 
2.0%
334.5802346 12
 
2.0%
276.2397231 12
 
2.0%
761.7411298 12
 
2.0%
1778.260666 12
 
2.0%
575.4370336 12
 
2.0%
Other values (40) 480
80.0%
ValueCountFrequency (%)
265.5593551 12
2.0%
276.2397231 12
2.0%
282.0073177 12
2.0%
320.0065173 12
2.0%
334.5802346 12
2.0%
358.7609616 12
2.0%
440.4774961 12
2.0%
484.0725608 12
2.0%
544.1827541 12
2.0%
575.4370336 12
2.0%
ValueCountFrequency (%)
3792.014206 12
2.0%
2478.318015 12
2.0%
2410.82536 12
2.0%
2371.200345 12
2.0%
2170.406711 12
2.0%
1899.329536 12
2.0%
1799.330922 12
2.0%
1778.260666 12
2.0%
1756.175787 12
2.0%
1689.360972 12
2.0%

Interactions

2025-07-27T01:11:25.846001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.111199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.443552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.847840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:08.535070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.322681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.636422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.267165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.742185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.049797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.703060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.138526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.827414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.747351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.462722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.932243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.195410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.530648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.926177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:08.665781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.406260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.739058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.363436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.834506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.137455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.797546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.225402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.953163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.830157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.551237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.025472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.279267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.621592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.012333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:08.793697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.500291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.833123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.478978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.924587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.229193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.892404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.317288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:21.093989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.921205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.644052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.115098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.372723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.709906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.085267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:08.905584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.581171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.916225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.566758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.999464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.309895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.993753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.401300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:21.217614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.998752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.731453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.201354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.462253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.801916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.162307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.041574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.668215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.005783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.661610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.084733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.394716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.089544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.489693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:21.342081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:23.087893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.824961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.300732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.550520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.889982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.237708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.171154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.758557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.097456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.753311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.168048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.484106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.176745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.578279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:21.469956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:23.180503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.909765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.393693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.636858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.981372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.320578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.307805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.846761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.187380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.861890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.251263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.570335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.272273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.671898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:21.606738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:23.271842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.002010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.490741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.729487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.080713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.414512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.456450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.937325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.282770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.961534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.343444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.665257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.373553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.769088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:21.763864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:23.365315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.102796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.577513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.810103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.171966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.504973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.589175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.018318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.365697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.055833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.426513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.745020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.468075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.867601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:21.890437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:23.451254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.189722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.664894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.892782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.262318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.586025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.740038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.104271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.459054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.153992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.510638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.829185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.558267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.018974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.031556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:23.538102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.290686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.757832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:03.983493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.357676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.672335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.868540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.198233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.808879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.253433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.601419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:16.934540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.653184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.169348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.193299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:23.627816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.382586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.850428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.079601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.471312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.753126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:09.958632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.285267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.898954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.352698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.689253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.025923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.748588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.301134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.338119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.106907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.477633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:26.943217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.173810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.571700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.835993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.050717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.372153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:12.993262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.454004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.772707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.118173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.842629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.432338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.474864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.192669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.573074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:27.028476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.255377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.661772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:06.917161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.139120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.459150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.082626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.550991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.863141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.516996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:18.931242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.558821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.562075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.289549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.660388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:27.128799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:04.342512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:05.753808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:07.003212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:10.229843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:11.547475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:13.177852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:14.650221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:15.958099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:17.613382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:19.042459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:20.694629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:22.657650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:24.376178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-27T01:11:25.749502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-27T01:11:31.753489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Account_BalanceAgeCustomer_IDDebt_PaymentsDebt_RatioEssential_SpendingGenderIncomeInvestment_RateInvestments_AmountMonthNon_Essential_SpendingSavings_AmountSavings_RateSpending_RateSpending_VolatilityTotal_Spending
Account_Balance1.0000.1820.449-0.215-0.259-0.0830.2150.111-0.028-0.0240.069-0.119-0.139-0.147-0.9410.473-0.210
Age0.1821.0000.966-0.322-0.3940.0360.2950.0380.1530.1520.0000.173-0.169-0.191-0.1680.260-0.054
Customer_ID0.4490.9661.0000.9570.9530.9410.9590.9660.8000.9410.0000.3190.8680.7440.3630.9640.687
Debt_Payments-0.215-0.3220.9571.0000.9470.1210.3420.354-0.094-0.0820.006-0.053-0.054-0.0830.309-0.1680.403
Debt_Ratio-0.259-0.3940.9530.9471.000-0.0460.4240.139-0.144-0.1590.012-0.160-0.114-0.1250.295-0.2640.221
Essential_Spending-0.0830.0360.9410.121-0.0461.0000.3880.7150.1640.2510.0020.2980.037-0.0180.3110.1200.757
Gender0.2150.2950.9590.3420.4240.3881.0000.3040.2800.3970.0000.1840.4410.3550.1480.3950.124
Income0.1110.0380.9660.3540.1390.7150.3041.0000.2920.3870.0000.4550.1080.0360.1810.3680.915
Investment_Rate-0.0280.1530.800-0.094-0.1440.1640.2800.2921.0000.978-0.0000.110-0.076-0.1120.1240.0870.328
Investments_Amount-0.0240.1520.941-0.082-0.1590.2510.3970.3870.9781.000-0.0010.148-0.011-0.0540.1480.1280.417
Month0.0690.0000.0000.0060.0120.0020.0000.000-0.000-0.0011.000-0.079-0.006-0.008-0.0690.000-0.024
Non_Essential_Spending-0.1190.1730.319-0.053-0.1600.2980.1840.4550.1100.148-0.0791.000-0.142-0.1730.2740.5940.477
Savings_Amount-0.139-0.1690.868-0.054-0.1140.0370.4410.108-0.076-0.011-0.006-0.1421.0000.9910.160-0.2480.172
Savings_Rate-0.147-0.1910.744-0.083-0.125-0.0180.3550.036-0.112-0.054-0.008-0.1730.9911.0000.145-0.2690.106
Spending_Rate-0.941-0.1680.3630.3090.2950.3110.1480.1810.1240.148-0.0690.2740.1600.1451.000-0.3400.491
Spending_Volatility0.4730.2600.964-0.168-0.2640.1200.3950.3680.0870.1280.0000.594-0.248-0.269-0.3401.0000.179
Total_Spending-0.210-0.0540.6870.4030.2210.7570.1240.9150.3280.417-0.0240.4770.1720.1060.4910.1791.000

Missing values

2025-07-27T01:11:27.275020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-27T01:11:27.445724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Customer_IDMonthAgeGenderIncomeTotal_SpendingEssential_SpendingNon_Essential_SpendingSavings_AmountInvestments_AmountDebt_PaymentsAccount_BalanceSavings_RateInvestment_RateDebt_RatioSpending_RateSpending_Volatility
0CUST1001123Male1133710696.365500.001728.96566.851700.551200.0640.640.050.150.1058480.943491598.619274
1CUST1001223Male1133710357.915900.00990.51566.851700.551200.0979.090.050.150.1058480.913638598.619274
2CUST1001323Male113379593.215198.11927.70566.851700.551200.01743.790.050.150.1058480.846186598.619274
3CUST1001423Male1133710932.085964.681500.00566.851700.551200.0404.920.050.150.1058480.964283598.619274
4CUST1001523Male1133711251.975884.571900.00566.851700.551200.085.030.050.150.1058480.992500598.619274
5CUST1001623Male1133710312.724226.412618.91566.851700.551200.01024.280.050.150.1058480.909652598.619274
6CUST1001723Male1133710863.966668.31728.25566.851700.551200.0473.040.050.150.1058480.958275598.619274
7CUST1001823Male1133710864.784879.912517.47566.851700.551200.0472.220.050.150.1058480.958347598.619274
8CUST1001923Male113379786.984744.531575.05566.851700.551200.01550.020.050.150.1058480.863278598.619274
9CUST10011023Male113379904.943646.932790.61566.851700.551200.01432.060.050.150.1058480.873683598.619274
Customer_IDMonthAgeGenderIncomeTotal_SpendingEssential_SpendingNon_Essential_SpendingSavings_AmountInvestments_AmountDebt_PaymentsAccount_BalanceSavings_RateInvestment_RateDebt_RatioSpending_RateSpending_Volatility
590CUST1048359Female2717127118.408550.06200.008151.32717.11500.052.600.30.10.0552060.9980641625.966066
591CUST1048459Female2717124340.598550.03422.198151.32717.11500.02830.410.30.10.0552060.8958301625.966066
592CUST1048559Female2717126262.708550.05344.308151.32717.11500.0908.300.30.10.0552060.9665711625.966066
593CUST1048659Female2717124790.348550.03871.948151.32717.11500.02380.660.30.10.0552060.9123821625.966066
594CUST1048759Female2717122601.498550.01683.098151.32717.11500.04569.510.30.10.0552060.8318241625.966066
595CUST1048859Female2717125485.408550.04567.008151.32717.11500.01685.600.30.10.0552060.9379631625.966066
596CUST1048959Female2717124666.868550.03748.468151.32717.11500.02504.140.30.10.0552060.9078381625.966066
597CUST10481059Female2717126374.408550.05456.008151.32717.11500.0796.600.30.10.0552060.9706821625.966066
598CUST10481159Female2717127118.408550.06200.008151.32717.11500.052.600.30.10.0552060.9980641625.966066
599CUST10481259Female2717124187.228550.03268.828151.32717.11500.02983.780.30.10.0552060.8901851625.966066